Sparse representation for recommendation. Learning sparse codes [1]}, [2]}, [3]}, [4]} is a widely adopted way to represent data objects in machine learning, inspiring a variety of studies related to product quantization [5]}, [6]}, [7]}, [8]}, multi-way compact embedding [9]}, [10]}, semantic hashing [11]}, etc. In contrast to continuous representation, it seeks a sparse scheme for capturing the most salient representation dimensions. Specifically, discrete representations are also applied in recommender systems [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, and existing studies mainly aim to develop efficient recommendation algorithms in both memory and time based on the sparse representations, so as to develop large-scale recommender systems [18]}, [19]}, [20]}, [21]}. Different from these studies, our work aims to leverage the generality of text semantics for learning transferable item representations based on a pre-trained approach.
